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ZTE MR-based attenuation correction in brain FDG-PET/MR: performance in patients with cognitive impairment

  • Nuclear Medicine
  • Published:
European Radiology Aims and scope Submit manuscript

Abstract

Objective

One of the main challenges of integrated PET/MR is to achieve an accurate PET attenuation correction (AC), especially in brain acquisition. Here, we evaluated an AC method based on zero echo time (ZTE) MRI, comparing it with the single-atlas AC method and CT-based AC, set as reference.

Methods

Fifty patients (70 ± 11 years old, 28 men) underwent FDG-PET/MR examination (SIGNA PET/MR 3.0 T, GE Healthcare) as part of the investigation of suspected dementia. They all had brain computed tomography (CT), 2-point LAVA-flex MRI (for atlas-based AC), and ZTE-MRI. Two AC methods were compared with CT-based AC (CTAC): one based on a single atlas, one based on ZTE segmentation. Impact on brain metabolism was evaluated using voxel and volumes of interest–based analyses. The impact of AC was also evaluated through comparisons between two subgroups of patients extracted from the whole population: 15 patients with mild cognitive impairment and normal metabolic pattern, and 22 others with metabolic pattern suggestive of Alzheimer disease, using SPM12 software.

Results

ZTE-AC yielded a lower bias (3.6 ± 3.2%) than the atlas method (4.5 ± 6.1%) and lowest interindividual (4.6% versus 6.8%) and inter-regional (1.4% versus 2.6%) variabilities. Atlas-AC resulted in metabolism overestimation in cortical regions near the vertex and cerebellum underestimation. ZTE-AC yielded a moderate metabolic underestimation mainly in the occipital cortex and cerebellum. Voxel-wise comparison between the two subgroups of patients showed that significant difference clusters had a slightly smaller size but similar locations with PET images corrected with ZTE-AC compared with those corrected with CT, whereas atlas-AC images showed a notable reduction of significant voxels.

Conclusion

ZTE-AC performed better than atlas-AC in detecting pathologic areas in suspected neurodegenerative dementia.

Key Points

• The ZTE-based AC improved the accuracy of the metabolism quantification in PET compared with the atlas-AC method.

• The overall uptake bias was 21% lower when using ZTE-based AC compared with the atlas-AC method.

• ZTE-AC performed better than atlas-AC in detecting pathologic areas in suspected neurodegenerative dementia.

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Abbreviations

AAL:

Automated anatomical labeling

AC:

Attenuation correction

AC-PC line:

Anterior commissure–posterior commissure line

AD:

Alzheimer disease

CNN:

Convolutional neural networks

DL:

Deep learning

FDG:

2-Fluoro-2-deoxy-d-glucose

FWE:

Family-wise error

HU:

Hounsfield unit

MNI:

Montreal Neurological Institute

MR:

Magnetic resonance

MRAC:

Magnetic resonance–based attenuation correction

MRI:

Magnetic resonance imaging

PET:

Positron emission tomography

PSF:

Point spread function

SPM:

Statistical parametric mapping

SUV:

Standard uptake value

TOF:

Time of flight

UTE:

Ultrashort time

ZTE:

Zero time echo

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Acknowledgements

The authors would like to thank GE Healthcare for providing access to research tools and prototype pulse sequences.

The authors also would like to thank ARC foundation which allowed Dr. SGARD to get a fellowship for a year of research during which he was able to carry out this study.

Funding

The authors state that this work has not received any funding.

Author information

Author notes

  1. Brian Sgard and Maya Khalifé contributed equally to this work.

Authors and Affiliations

  1. Department of Nuclear Medicine, Groupe Hospitalier Pitié-Salpêtrière Charles Foix, 75013, APHP, Paris, France

    Brian Sgard, Marine Soret, Marie-Odile Habert & Aurélie Kas

  2. Laboratoire d’Imagerie Biomédicale (LIB), Sorbonne Université, CNRS, INSERM, 75006, Paris, France

    Brian Sgard, Marine Soret, Alain Giron, Marie-Odile Habert & Aurélie Kas

  3. Centre de NeuroImagerie de Recherche (CENIR), Institut du Cerveau et de la Moelle épinière (ICM), Paris, France

    Maya Khalifé & Arthur Bouchut

  4. Applications and Workflow, GE Healthcare, Orsay, France

    Brice Fernandez

  5. Department of Biophysics, Sorbonne Université, 75013, Paris, France

    Clara Zaslavsky

  6. Applications and Workflow, GE Healthcare, Cambridge, UK

    Gaspar Delso

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  1. Brian Sgard
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Corresponding author

Correspondence to Brian Sgard.

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Guarantor

The scientific guarantor of this publication is Aurélie Kas, MD, PhD, Department of Nuclear Medicine, Pitié-Salpêtrière C. Foix Hospital, APHP, Paris, France. Phone: 33 1 42 17 62 80. Fax: 33 1 42 17 62 92. Email: aurelie.kas@gmail.com

Conflict of interest

The authors of this manuscript declare relationships with the following companies:

Maya Khalifé received a research grant from GE Healthcare.

Brice Fernandez and Gaspar Delso are GE Healthcare employees. Only non-GE employees had control of inclusion of data and information that might present a conflict of interest for authors who are employees of GE Healthcare. No other potential conflict of interest relevant to this article was reported.

Aurélie Kas received honoria for lectures from GE Healthcare and Piramal.

Marie-Odile Habert received honoraria for lectures from Lilly.

Statistics and biometry

One of the authors has significant statistical expertise.

No complex statistical methods were necessary for this paper.

Informed consent

Written informed consent was obtained from all subjects (patients) in this study.

Ethical approval

Data of this study were extracted from the PET/MR examinations database of the Pitié-Salpêtrière Hospital, Paris, France, which was approved by the French authority for the protection of privacy and personal data in clinical research (CNIL, approval no. 2111722). All procedures performed in this study were in accordance with the ethical standards of the institutional research committee and with the 1964 Helsinki Declaration and its later amendments.

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Sgard, B., Khalifé, M., Bouchut, A. et al. ZTE MR-based attenuation correction in brain FDG-PET/MR: performance in patients with cognitive impairment. Eur Radiol 30, 1770–1779 (2020). https://doi.org/10.1007/s00330-019-06514-z

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  • DOI: https://doi.org/10.1007/s00330-019-06514-z

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